{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### News"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Placeholder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Placeholder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Unsloth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "2eSvM9zX_2d3",
        "outputId": "d0b6c66e-ab67-4241-bdf1-b1babc18c2d6"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c95f8f778a9844debb61f5d29bafc0eb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/1.14k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth: Fast Llama patching release 2024.4\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.2.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. Xformers = 0.0.25.post1. FA = False.\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n"
          ]
        },
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              "version_minor": 0
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            "text/plain": [
              "model.safetensors:   0%|          | 0.00/5.70G [00:00<?, ?B/s]"
            ]
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        },
        {
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            "text/plain": [
              "generation_config.json:   0%|          | 0.00/131 [00:00<?, ?B/s]"
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          },
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          "output_type": "display_data"
        },
        {
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              "model_id": "065928bbb1844d828f752d9aeec4d438",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/50.6k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "5452a280b4554784a9517e62bdd04714",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "814512fef9164353b858bd62e2f562c6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/449 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "max_seq_length = 4096 # Choose any! We auto support RoPE Scaling internally!\n",
        "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
        "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
        "\n",
        "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
        "fourbit_models = [\n",
        "    \"unsloth/mistral-7b-bnb-4bit\",\n",
        "    \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n",
        "    \"unsloth/llama-2-7b-bnb-4bit\",\n",
        "    \"unsloth/gemma-7b-bnb-4bit\",\n",
        "    \"unsloth/gemma-7b-it-bnb-4bit\", # Instruct version of Gemma 7b\n",
        "    \"unsloth/gemma-2b-bnb-4bit\",\n",
        "    \"unsloth/gemma-2b-it-bnb-4bit\", # Instruct version of Gemma 2b\n",
        "    \"unsloth/llama-3-8b-bnb-4bit\", # [NEW] 15 Trillion token Llama-3\n",
        "] # More models at https://huggingface.co/unsloth\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/llama-3-8b-bnb-4bit\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_H6EWbe-t_bk"
      },
      "source": [
        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bZsfBuZDeCL",
        "outputId": "34834b73-2386-45f5-d197-7f610106a053"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth 2024.4 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
        "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
        "    lora_alpha = 16,\n",
        "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
        "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
        "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
        "    random_state = 3407,\n",
        "    use_rslora = False,  # We support rank stabilized LoRA\n",
        "    loftq_config = None, # And LoftQ\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNfc6OLOuuyZ"
      },
      "source": [
        "<a name=\"Data\"></a>\n",
        "### Data Prep\n",
        "We now use a special ORPO style dataset from [recipe-research](https://huggingface.co/datasets/reciperesearch/dolphin-sft-v0.1-preference).\n",
        "\n",
        "You need at least 3 columns:\n",
        "* Instruction\n",
        "* Accepted\n",
        "* Rejected\n",
        "\n",
        "For example:\n",
        "* Instruction: \"What is 2+2?\"\n",
        "* Accepted: \"The answer is 4\"\n",
        "* Rejected: \"The answer is 5\"\n",
        "\n",
        "The goal of ORPO is to penalize the \"rejected\" samples, and increase the likelihood of \"accepted\" samples. [recipe-research](https://huggingface.co/datasets/reciperesearch/dolphin-sft-v0.1-preference) essentially used Mistral to generate the \"rejected\" responses, and used GPT-4 to generated the \"accepted\" responses."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          ]
        },
        "id": "GetrTh37qgDp",
        "outputId": "ab229333-42b5-45d7-fa01-f474622ae962"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4b617d72867444ca9da6ab3570a3eac8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/490 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "13a59e79b8c1400aabffe376477223b9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/34.1M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f43beff2d5e742e28208a19bd326ce39",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/16000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3be775e3950349afac0d1677db27d36d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/16000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# The data must be formatted with appropriate prompt template first.\n",
        "# See details here: https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py\n",
        "\n",
        "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction:\n",
        "{}\n",
        "\n",
        "### Input:\n",
        "{}\n",
        "\n",
        "### Response:\n",
        "{}\"\"\"\n",
        "\n",
        "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
        "\n",
        "def format_prompt(sample):\n",
        "    instruction = sample[\"instruction\"]\n",
        "    input       = sample[\"input\"]\n",
        "    accepted    = sample[\"accepted\"]\n",
        "    rejected    = sample[\"rejected\"]\n",
        "\n",
        "    # ORPOTrainer expects prompt/chosen/rejected keys\n",
        "    # See: https://huggingface.co/docs/trl/main/en/orpo_trainer\n",
        "    sample[\"prompt\"]   = alpaca_prompt.format(instruction, input, \"\")\n",
        "    sample[\"chosen\"]   = accepted + EOS_TOKEN\n",
        "    sample[\"rejected\"] = rejected + EOS_TOKEN\n",
        "    return sample\n",
        "pass\n",
        "\n",
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"reciperesearch/dolphin-sft-v0.1-preference\")[\"train\"]\n",
        "dataset = dataset.map(format_prompt,)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7PH-7Cudu8Ja"
      },
      "source": [
        "Let's print out some examples to see how the dataset should look like"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oF63zQqNlNJC",
        "outputId": "53cf0030-72b1-4654-9034-2317cdf122c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INSTRUCTION: ==================================================\n",
            "('Below is an instruction that describes a task, paired with an input that '\n",
            " 'provides further context. Write a response that appropriately completes the '\n",
            " 'request.\\n'\n",
            " '\\n'\n",
            " '### Instruction:\\n'\n",
            " 'You are an AI assistant that helps people find information.\\n'\n",
            " '\\n'\n",
            " '### Input:\\n'\n",
            " 'Given the rationale, provide a reasonable question and answer. Step-by-step '\n",
            " 'reasoning process: Xkcd comics are very popular amongst internet users.\\n'\n",
            " ' The question and answer:\\n'\n",
            " '\\n'\n",
            " '### Response:\\n')\n",
            "ACCEPTED: ==================================================\n",
            "('Question: What makes Xkcd comics popular among internet users?\\n'\n",
            " '\\n'\n",
            " 'Answer: Xkcd comics are popular among internet users because of their clever '\n",
            " 'humor, relatable themes, and minimalist art style. They often cover topics '\n",
            " 'like science, technology, and life experiences, making them appealing to a '\n",
            " 'broad audience.<|end_of_text|>')\n",
            "REJECTED: ==================================================\n",
            "('Question: What is the reason behind the popularity of Xkcd comics among '\n",
            " 'internet users?\\n'\n",
            " '\\n'\n",
            " 'Answer: Xkcd comics are popular among internet users because they offer a '\n",
            " 'unique blend of humor, relatable content, and thought-provoking topics that '\n",
            " 'resonate with a wide range of people. The comics often address everyday '\n",
            " 'experiences, technology, and social issues, making them accessible and '\n",
            " 'enjoyable for many individuals. Additionally, the simple and minimalistic '\n",
            " 'art style of Xkcd comics allows for easy comprehension and sharing, '\n",
            " 'contributing to their widespread appeal.<|end_of_text|>')\n"
          ]
        }
      ],
      "source": [
        "import pprint\n",
        "\n",
        "row = dataset[1]\n",
        "print(\"INSTRUCTION: \" + \"=\" * 50)\n",
        "pprint.pprint(row[\"prompt\"])\n",
        "print(\"ACCEPTED: \" + \"=\" * 50)\n",
        "pprint.pprint(row[\"chosen\"])\n",
        "print(\"REJECTED: \" + \"=\" * 50)\n",
        "pprint.pprint(row[\"rejected\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oqyleKojqgDq"
      },
      "outputs": [],
      "source": [
        "# Enable reward modelling stats\n",
        "from unsloth import PatchDPOTrainer\n",
        "\n",
        "PatchDPOTrainer()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1ZlnJpkxIuV"
      },
      "source": [
        "<a name=\"Train\"></a>\n",
        "### Train the model\n",
        "Now let's use Huggingface TRL's `ORPOTrainer`! More docs here: [TRL ORPO docs](https://huggingface.co/docs/trl/main/en/orpo_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122,
          "referenced_widgets": [
            "a11ecbe5a63a495683f37eeb9c59bf31",
            "e01a575bbfd84ccd9c6acb628632d848",
            "10150def49574b8c9697d69b3068df4a",
            "a48e289710e84e1192a8a85175e517a4",
            "fe0a86aa30134213a613bab9407a10f3",
            "6eb5b5bfae5140df89e32e7868ab4e98",
            "7f0eaebaebb944d99a291119dac94faf",
            "ca1bae6d505c4513bddb1666478adec8",
            "c2ee270cf91f4eee8354b2a056d997ce",
            "7cef798c923c471f87ddb35f7fa886b5",
            "f5872532493342b68457e165ca3bc255"
          ]
        },
        "id": "QtoqUw80QDV0",
        "outputId": "509a9b9f-dac1-4dd8-86b5-320af93a45ab"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/orpo_trainer.py:247: UserWarning: When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments we have set it for you, but you should do it yourself in the future.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a11ecbe5a63a495683f37eeb9c59bf31",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/16000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ],
      "source": [
        "from trl import ORPOConfig, ORPOTrainer\n",
        "from unsloth import is_bfloat16_supported\n",
        "\n",
        "orpo_trainer = ORPOTrainer(\n",
        "    model = model,\n",
        "    train_dataset = dataset,\n",
        "    tokenizer = tokenizer,\n",
        "    args = ORPOConfig(\n",
        "        max_length = max_seq_length,\n",
        "        max_prompt_length = max_seq_length//2,\n",
        "        max_completion_length = max_seq_length//2,\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        beta = 0.1,\n",
        "        logging_steps = 1,\n",
        "        optim = \"adamw_8bit\",\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        max_steps = 30, # Change to num_train_epochs = 1 for full training runs\n",
        "        fp16 = not is_bfloat16_supported(),\n",
        "        bf16 = is_bfloat16_supported(),\n",
        "        output_dir = \"outputs\",\n",
        "        report_to = \"none\", # Use this for WandB etc\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EWGFqAo5Q2me",
        "outputId": "34e5a57e-fe2a-4b44-99fc-cdbccf669d70"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 16,000 | Num Epochs = 1\n",
            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
            "\\        /    Total batch size = 8 | Total steps = 30\n",
            " \"-____-\"     Number of trainable parameters = 41,943,040\n",
            "Could not estimate the number of tokens of the input, floating-point operations will not be computed\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [30/30 10:33, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>rewards / chosen</th>\n",
              "      <th>rewards / rejected</th>\n",
              "      <th>rewards / accuracies</th>\n",
              "      <th>rewards / margins</th>\n",
              "      <th>logps / rejected</th>\n",
              "      <th>logps / chosen</th>\n",
              "      <th>logits / rejected</th>\n",
              "      <th>logits / chosen</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>5.696200</td>\n",
              "      <td>-0.314192</td>\n",
              "      <td>-0.139184</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.175007</td>\n",
              "      <td>-1.391845</td>\n",
              "      <td>-3.141916</td>\n",
              "      <td>-0.577899</td>\n",
              "      <td>-0.558586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>4.613900</td>\n",
              "      <td>-0.310306</td>\n",
              "      <td>-0.160126</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>-0.150180</td>\n",
              "      <td>-1.601255</td>\n",
              "      <td>-3.103056</td>\n",
              "      <td>-0.939558</td>\n",
              "      <td>-0.772177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>5.087600</td>\n",
              "      <td>-0.215214</td>\n",
              "      <td>-0.194589</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>-0.020624</td>\n",
              "      <td>-1.945894</td>\n",
              "      <td>-2.152137</td>\n",
              "      <td>-0.750424</td>\n",
              "      <td>-0.748731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>3.823400</td>\n",
              "      <td>-0.288235</td>\n",
              "      <td>-0.289640</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.001405</td>\n",
              "      <td>-2.896399</td>\n",
              "      <td>-2.882351</td>\n",
              "      <td>-0.972239</td>\n",
              "      <td>-1.037048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>3.636200</td>\n",
              "      <td>-0.138244</td>\n",
              "      <td>-0.123302</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>-0.014942</td>\n",
              "      <td>-1.233017</td>\n",
              "      <td>-1.382437</td>\n",
              "      <td>-0.742390</td>\n",
              "      <td>-0.715153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.950500</td>\n",
              "      <td>-0.226424</td>\n",
              "      <td>-0.291118</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.064694</td>\n",
              "      <td>-2.911181</td>\n",
              "      <td>-2.264242</td>\n",
              "      <td>-0.931786</td>\n",
              "      <td>-0.948551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.191200</td>\n",
              "      <td>-0.178283</td>\n",
              "      <td>-0.201864</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.023581</td>\n",
              "      <td>-2.018638</td>\n",
              "      <td>-1.782828</td>\n",
              "      <td>-1.227170</td>\n",
              "      <td>-1.240975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.057900</td>\n",
              "      <td>-0.139332</td>\n",
              "      <td>-0.110689</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>-0.028643</td>\n",
              "      <td>-1.106888</td>\n",
              "      <td>-1.393321</td>\n",
              "      <td>-1.249107</td>\n",
              "      <td>-1.263795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.147400</td>\n",
              "      <td>-0.134407</td>\n",
              "      <td>-0.099334</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>-0.035072</td>\n",
              "      <td>-0.993341</td>\n",
              "      <td>-1.344065</td>\n",
              "      <td>-1.282116</td>\n",
              "      <td>-1.354808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.837500</td>\n",
              "      <td>-0.212703</td>\n",
              "      <td>-0.186428</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.026275</td>\n",
              "      <td>-1.864279</td>\n",
              "      <td>-2.127032</td>\n",
              "      <td>-1.162032</td>\n",
              "      <td>-1.108897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.933000</td>\n",
              "      <td>-0.156348</td>\n",
              "      <td>-0.097927</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.058421</td>\n",
              "      <td>-0.979267</td>\n",
              "      <td>-1.563482</td>\n",
              "      <td>-1.190825</td>\n",
              "      <td>-1.245409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.973500</td>\n",
              "      <td>-0.200500</td>\n",
              "      <td>-0.159612</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.040889</td>\n",
              "      <td>-1.596117</td>\n",
              "      <td>-2.005002</td>\n",
              "      <td>-1.380260</td>\n",
              "      <td>-1.329627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>1.634200</td>\n",
              "      <td>-0.203949</td>\n",
              "      <td>-0.153286</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>-0.050662</td>\n",
              "      <td>-1.532863</td>\n",
              "      <td>-2.039488</td>\n",
              "      <td>-1.185556</td>\n",
              "      <td>-1.275752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>1.876500</td>\n",
              "      <td>-0.127660</td>\n",
              "      <td>-0.088646</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>-0.039013</td>\n",
              "      <td>-0.886463</td>\n",
              "      <td>-1.276598</td>\n",
              "      <td>-1.225720</td>\n",
              "      <td>-1.241006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.584200</td>\n",
              "      <td>-0.146653</td>\n",
              "      <td>-0.123118</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.023535</td>\n",
              "      <td>-1.231185</td>\n",
              "      <td>-1.466535</td>\n",
              "      <td>-1.250568</td>\n",
              "      <td>-1.257678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>1.752800</td>\n",
              "      <td>-0.135002</td>\n",
              "      <td>-0.080984</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.054018</td>\n",
              "      <td>-0.809841</td>\n",
              "      <td>-1.350021</td>\n",
              "      <td>-1.202422</td>\n",
              "      <td>-1.211266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>1.627300</td>\n",
              "      <td>-0.099521</td>\n",
              "      <td>-0.067955</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.031566</td>\n",
              "      <td>-0.679553</td>\n",
              "      <td>-0.995214</td>\n",
              "      <td>-1.287499</td>\n",
              "      <td>-1.307789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>1.800100</td>\n",
              "      <td>-0.123857</td>\n",
              "      <td>-0.085257</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>-0.038600</td>\n",
              "      <td>-0.852571</td>\n",
              "      <td>-1.238573</td>\n",
              "      <td>-1.288435</td>\n",
              "      <td>-1.280339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>1.776000</td>\n",
              "      <td>-0.107769</td>\n",
              "      <td>-0.076882</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>-0.030888</td>\n",
              "      <td>-0.768817</td>\n",
              "      <td>-1.077694</td>\n",
              "      <td>-1.211875</td>\n",
              "      <td>-1.201562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.480900</td>\n",
              "      <td>-0.113957</td>\n",
              "      <td>-0.082669</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>-0.031288</td>\n",
              "      <td>-0.826692</td>\n",
              "      <td>-1.139575</td>\n",
              "      <td>-1.079398</td>\n",
              "      <td>-1.131909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>1.593300</td>\n",
              "      <td>-0.088314</td>\n",
              "      <td>-0.054479</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.033835</td>\n",
              "      <td>-0.544790</td>\n",
              "      <td>-0.883143</td>\n",
              "      <td>-1.072568</td>\n",
              "      <td>-1.130443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>1.754200</td>\n",
              "      <td>-0.103276</td>\n",
              "      <td>-0.097119</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>-0.006157</td>\n",
              "      <td>-0.971189</td>\n",
              "      <td>-1.032762</td>\n",
              "      <td>-1.134647</td>\n",
              "      <td>-1.186859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>1.376700</td>\n",
              "      <td>-0.096716</td>\n",
              "      <td>-0.079258</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>-0.017458</td>\n",
              "      <td>-0.792582</td>\n",
              "      <td>-0.967161</td>\n",
              "      <td>-1.022214</td>\n",
              "      <td>-1.007320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>1.642400</td>\n",
              "      <td>-0.106709</td>\n",
              "      <td>-0.072481</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>-0.034228</td>\n",
              "      <td>-0.724806</td>\n",
              "      <td>-1.067085</td>\n",
              "      <td>-0.955501</td>\n",
              "      <td>-1.042999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.424300</td>\n",
              "      <td>-0.135257</td>\n",
              "      <td>-0.081045</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>-0.054212</td>\n",
              "      <td>-0.810449</td>\n",
              "      <td>-1.352572</td>\n",
              "      <td>-1.015064</td>\n",
              "      <td>-1.071248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>1.365000</td>\n",
              "      <td>-0.109444</td>\n",
              "      <td>-0.079274</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>-0.030171</td>\n",
              "      <td>-0.792736</td>\n",
              "      <td>-1.094441</td>\n",
              "      <td>-1.103141</td>\n",
              "      <td>-1.140949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>1.529900</td>\n",
              "      <td>-0.126537</td>\n",
              "      <td>-0.088402</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>-0.038135</td>\n",
              "      <td>-0.884018</td>\n",
              "      <td>-1.265368</td>\n",
              "      <td>-0.991220</td>\n",
              "      <td>-1.012082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.388800</td>\n",
              "      <td>-0.101727</td>\n",
              "      <td>-0.065627</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.036099</td>\n",
              "      <td>-0.656274</td>\n",
              "      <td>-1.017269</td>\n",
              "      <td>-0.894547</td>\n",
              "      <td>-0.934883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>1.828400</td>\n",
              "      <td>-0.115963</td>\n",
              "      <td>-0.076427</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.039536</td>\n",
              "      <td>-0.764271</td>\n",
              "      <td>-1.159630</td>\n",
              "      <td>-1.092558</td>\n",
              "      <td>-1.179406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.660200</td>\n",
              "      <td>-0.144630</td>\n",
              "      <td>-0.061597</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.083033</td>\n",
              "      <td>-0.615965</td>\n",
              "      <td>-1.446295</td>\n",
              "      <td>-0.883829</td>\n",
              "      <td>-1.028757</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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          "data": {
            "text/plain": [
              "TrainOutput(global_step=30, training_loss=2.234786526362101, metrics={'train_runtime': 665.6376, 'train_samples_per_second': 0.361, 'train_steps_per_second': 0.045, 'total_flos': 0.0, 'train_loss': 2.234786526362101, 'epoch': 0.015})"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "orpo_trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FgEvCW76xblp"
      },
      "source": [
        "<a name=\"Inference\"></a>\n",
        "### Inference\n",
        "Let's run the model! You can change the instruction and input - leave the output blank!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0DJPbbtGxcFJ",
        "outputId": "6b25cdda-c86f-4f25-99e7-90c170d97197"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "['<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13<|end_of_text|>']"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model)  # Enable native 2x faster inference\n",
        "inputs = tokenizer(\n",
        "    [\n",
        "        alpaca_prompt.format(\n",
        "            \"Continue the fibonnaci sequence.\",  # instruction\n",
        "            \"1, 1, 2, 3, 5, 8\",  # input\n",
        "            \"\",  # output - leave this blank for generation!\n",
        "        )\n",
        "    ],\n",
        "    return_tensors=\"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True)\n",
        "tokenizer.batch_decode(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "absTV8M1xzwz"
      },
      "source": [
        " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xKzWRN1Px0Bg",
        "outputId": "fcbe0342-a227-4a1d-be7b-039f37193ec3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
            "\n",
            "### Instruction:\n",
            "Continue the fibonnaci sequence.\n",
            "\n",
            "### Input:\n",
            "1, 1, 2, 3, 5, 8\n",
            "\n",
            "### Response:\n",
            "13<|end_of_text|>\n"
          ]
        }
      ],
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model)  # Enable native 2x faster inference\n",
        "inputs = tokenizer(\n",
        "    [\n",
        "        alpaca_prompt.format(\n",
        "            \"Continue the fibonnaci sequence.\",  # instruction\n",
        "            \"1, 1, 2, 3, 5, 8\",  # input\n",
        "            \"\",  # output - leave this blank for generation!\n",
        "        )\n",
        "    ],\n",
        "    return_tensors=\"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "\n",
        "text_streamer = TextStreamer(tokenizer)\n",
        "_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=128)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y_3rdZXmx3Hh"
      },
      "source": [
        "<a name=\"Save\"></a>\n",
        "### Saving, loading finetuned models\n",
        "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
        "\n",
        "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJbRqLynx3a8",
        "outputId": "05228b0d-4490-49f1-cd71-5cfe0f9417c9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('lora_model/tokenizer_config.json',\n",
              " 'lora_model/special_tokens_map.json',\n",
              " 'lora_model/tokenizer.json')"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.save_pretrained(\"lora_model\")  # Local saving\n",
        "tokenizer.save_pretrained(\"lora_model\")\n",
        "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
        "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mwIRb8DByBGg"
      },
      "source": [
        "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPHJs9wDyBbN",
        "outputId": "77165645-7402-4347-a613-f0ad90b572d9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[\"<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nWhat is a famous tall tower in Paris?\\n\\n### Input:\\n\\n\\n### Response:\\nThe Eiffel Tower is a famous tall tower in Paris. It is a wrought iron tower located on the Champ de Mars in Paris, France. The tower is named after the engineer Gustave Eiffel, the main designer, and was built as the entrance to the 1889 World's Fair. The tower\"]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "if False:\n",
        "    from unsloth import FastLanguageModel\n",
        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "        model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "        max_seq_length = max_seq_length,\n",
        "        dtype = dtype,\n",
        "        load_in_4bit = load_in_4bit,\n",
        "    )\n",
        "    FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "\n",
        "# alpaca_prompt = You MUST copy from above!\n",
        "\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_prompt.format(\n",
        "        \"What is a famous tall tower in Paris?\", # instruction\n",
        "        \"\", # input\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    )\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
        "tokenizer.batch_decode(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPSrEDuoyE5_"
      },
      "source": [
        "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sTO5VnItyC08"
      },
      "outputs": [],
      "source": [
        "if False:\n",
        "    # I highly do NOT suggest - use Unsloth if possible\n",
        "    from peft import AutoPeftModelForCausalLM\n",
        "    from transformers import AutoTokenizer\n",
        "    model = AutoPeftModelForCausalLM.from_pretrained(\n",
        "        \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "        load_in_4bit = load_in_4bit,\n",
        "    )\n",
        "    tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l10uNsFYyGav"
      },
      "source": [
        "### Saving to float16 for VLLM\n",
        "\n",
        "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fZR6n7DsyHu6"
      },
      "outputs": [],
      "source": [
        "# Merge to 16bit\n",
        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n",
        "\n",
        "# Merge to 4bit\n",
        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
        "\n",
        "# Just LoRA adapters\n",
        "if False:\n",
        "    model.save_pretrained(\"model\")\n",
        "    tokenizer.save_pretrained(\"model\")\n",
        "if False:\n",
        "    model.push_to_hub(\"hf/model\", token = \"\")\n",
        "    tokenizer.push_to_hub(\"hf/model\", token = \"\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ssjW3ST2yI1L"
      },
      "source": [
        "### GGUF / llama.cpp Conversion\n",
        "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
        "\n",
        "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
        "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
        "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
        "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FKVTNAwyyKFR"
      },
      "outputs": [],
      "source": [
        "# Save to 8bit Q8_0\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
        "\n",
        "# Save to 16bit GGUF\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n",
        "\n",
        "# Save to q4_k_m GGUF\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")"
      ]
    }
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